Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase.

Bioluminescent indicators are power tools for studying dynamic biological processes. In this study, we present the generation of novel bioluminescent indicators by modifying the luciferin molecule with an analyte-binding moiety. Specifically, we have successfully developed the first bioluminescent indicator for potassium ions (K+), which are critical electrolytes in biological systems. Our approach involved the design and synthesis of a K+-binding luciferin named potassiorin. Additionally, we engineered a luciferase enzyme called BRIPO (bioluminescent red indicator for potassium) to work synergistically with potassiorin, resulting in optimized K+-dependent bioluminescence responses. Through extensive validation in cell lines, primary neurons, and live mice, we demonstrated the efficacy of this new tool for detecting K+. Our research demonstrates an innovative concept of incorporating sensory moieties into luciferins to modulate luciferase activity. This approach has great potential for developing a wide range of bioluminescent indicators, advancing bioluminescence imaging (BLI), and enabling the study of various analytes in biological systems.

A fast, high-affinity fluorescent serotonin biosensor engineered from a tick lipocalin.

Serotonin (5-HT) is an important signaling monoamine and neurotransmitter. We report structure-guided engineering of a green fluorescent, genetically encoded serotonin sensor (G-GESS) from a 5-HT-binding lipocalin in the soft tick Argas monolakensis. G-GESS shows fast response kinetics and high affinity, specificity, brightness and photostability. We used G-GESS to image 5-HT dynamics in cultured cells, brain slices and behaving mice.

Enable the Domino-Like Structural Recovering in Bismuth Anode to Achieve Fast and Durable Na/K Storages.

Alloying-type anodes show capacity and density advantages for sodium/potassium-ion batteries (SIBs/PIBs), but they encounter serious structural degradation upon cycling, which cannot be resolved through conventional nanostructuring techniques. Herein, we present an in-depth study to reveal the intrinsic reason for the pulverization of bismuth (Bi) materials upon (de)alloying, and report a novel particle-in-bulk architecture with Bi nanospheres inlaid in the bulk carbon (BiNC) to achieve durable Na/K storage. We simulate the volume-expansion-resistant mechanism of Bi during the (de)alloying reaction, and unveil that the irreversible phase transition upon (de)alloying underlies the fundamental origin for the structural degradation of Bi anode, while a proper compressive stress (~10 %) raised by the bulk carbon can trigger a "domino-like" Bi crystal recovering. Consequently, the as obtained BiNC exhibits a record high volumetric capacity (823.1 mAh cm-3 for SIBs, 848.1 mAh cm-3 for PIBs) and initial coulombic efficiency (95.3 % for SIBs, 96.4 % for PIBs), and unprecedented cycling stability (15000 cycles for SIBs with only 0.0015 % degradation per cycle), outperforming the state-of-the-art literature. This work provides new insights on the undesirable structural evolution, and proposes basic guidelines for design of the anti-degradation structure for alloy-type electrode materials.

Land Use and Land Cover Classification Meets Deep Learning: A Review.

As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth's surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.

A Stress Self-Adaptive Structure to Suppress the Chemo-mechanical Degradation for High Rate and Ultralong Cycle Life Sodium Ion Batteries.

Transition-metal phosphides (TMPs) as typical conversion-type anode materials demonstrate extraordinary theoretical charge storage capacity for sodium ion batteries, but the unavoidable volume expansion and irreversible capacity loss upon cycling represent their long-standing limitations. Herein we report a stress self-adaptive structure with ultrafine FeP nanodots embedded in dense carbon microplates skeleton (FeP@CMS) via the spatial confinement of carbon quantum dots (CQDs). Such an architecture delivers a record high specific capacity (778 mAh g-1 at 0.05 A g-1 ) and ultra-long cycle stability (87.6 % capacity retention after 10 000 cycles at 20 A g-1 ), which outperform the state-of-the-art literature. We decode the fundamental reasons for this unprecedented performance, that such an architecture allows the spontaneous stress transfer from FeP nanodots to the surrounding carbon matrix, thus overcomes the intrinsic chemo-mechanical degradation of metal phosphides.

Inhibitory effects of flavonoids on P-glycoprotein in vitro and in vivo: Food/herb-drug interactions and structure-activity relationships.

Flavonoids are a class of polyphenol antioxygen, despite various known biological activities and therapeutic potential, scattered but not much is known about their interactions with drug transporters. P-glycoprotein (P-gp) as a cellular defense mechanism by effluxing its substrates has been widely investigated. The aim of this study was to investigate the inhibitory effects of 75 flavonoids on P-gp in vitro and in vivo and to illuminate the structure-activity relationships of flavonoids with P-gp. Five flavonoids, including tangeretin, sinensetin, isosinensetin, sciadopitysin and oroxylin A exhibited significant inhibition on P-gp in MDR1-MDCKIIcells, which reduced the P-gp-mediated efflux of paraquat and taxol and consequently increased their cell toxicity. In addition, co-administration of digoxin with five flavonoids increased the AUC0-t of digoxin in different extents in rats, from 19.84% to 81.51%. Molecular docking assays elucidated the inhibitory effect of flavonoids might be related to Pi interactions, but not hydrogen bonds. The pharmacophore model suggested the hydrophobic groups in B benzene ring may play a vital role in the potency of flavonoids inhibition on P-gp. Taken together, our findings would provide the basis for a reliable assessment of the potential risks of flavonoid-containing food/herb-drug interactions in humans.

Seeing the Unseen of the Combination of Two Natural Resins, Frankincense and Myrrh: Changes in Chemical Constituents and Pharmacological Activities.

For the treatment of diseases, especially chronic diseases, traditional natural drugs have more effective therapeutic advantages because of their multi-target and multi-channel characteristics. Among many traditional natural medicines, resins frankincense and myrrh have been proven to be effective in the treatment of inflammation and cancer. In the West, frankincense and myrrh have been used as incense in religious and cultural ceremonies since ancient times; in traditional Chinese and Ayurvedic medicine, they are used mainly for the treatment of chronic diseases. The main chemical constituents of frankincense and myrrh are terpenoids and essential oils. Their common pharmacological effects are anti-inflammatory and anticancer. More interestingly, in traditional Chinese medicine, frankincense and myrrh have been combined as drug pairs in the same prescription for thousands of years, and their combination has a better therapeutic effect on diseases than a single drug. After the combination of frankincense and myrrh forms a blend, a series of changes take place in their chemical composition, such as the increase or decrease of the main active ingredients, the disappearance of native chemical components, and the emergence of new chemical components. At the same time, the pharmacological effects of the combination seem magically powerful, such as synergistic anti-inflammation, synergistic anticancer, synergistic analgesic, synergistic antibacterial, synergistic blood-activation, and so on. In this review, we summarize the latest research on the main chemical constituents and pharmacological activities of these two natural resins, along with chemical and pharmacological studies on the combination of the two.

Advancements in inhibitors of crucial enzymes in the cysteine biosynthetic pathway: Serine acetyltransferase and O-acetylserine sulfhydrylase.

Infectious diseases have been jeopardized problem that threaten public health over a long period of time. The growing prevalence of drug-resistant pathogens and infectious cases have led to a decrease in the number of effective antibiotics, which highlights the urgent need for the development of new antibacterial agents. Serine acetyltransferase (SAT), also known as CysE in certain bacterial species, and O-acetylserine sulfhydrylase (OASS), also known as CysK in select bacteria, are indispensable enzymes within the cysteine biosynthesis pathway of various pathogenic microorganisms. These enzymes play a crucial role in the survival of these pathogens, making SAT and OASS promising targets for the development of novel anti-infective agents. In this comprehensive review, we present an introduction to the structure and function of SAT and OASS, along with an overview of existing inhibitors for SAT and OASS as potential antibacterial agents. Our primary focus is on elucidating the inhibitory activities, structure-activity relationships, and mechanisms of action of these inhibitors. Through this exploration, we aim to provide insights into promising strategies and prospects in the development of antibacterial agents that target these essential enzymes.

Fostering resilience and post-traumatic growth in overseas Chinese left-behind children: The role of autonomy, competence, and relatedness.

OBJECTIVE: This study examines the impact of parental migration on the psychological well-being and development of left-behind children (LBCs) in Zhejiang, China, within the broader context of the country's rural transformations and urban migration. It investigates how intellectual and relational engagement (RE), autonomy (AUT), competence (COM), and relatedness (RES) contribute to resilience (REL) and post-traumatic growth (PTG) in these children, reflecting on the shift from viewing parental separation merely as a source of trauma to recognizing its potential to foster significant personal growth. METHODS: Utilizing a cross-sectional design, the research was conducted in April and May 2023 with 1348 LBCs from a total sample of 4049 students inZhejiang. A two-step random, stratified, cluster-based sampling strategy was employed, and structural equation modeling was used to examine the hypothesized relationships among the constructs. RESULTS: The statistical analysis demonstrated significant positive effects of intellectual engagement (IE), AUT, COM, and RE on both REL and PTG (p < .05 for all). IE strongly correlated with AUT (r = .68, p < .001) and COM (r = .71, p < .001), enhancing REL and facilitating PTG. Additionally, the presence of secure and consistent relationships was identified as crucial for maintaining psychological well-being, with high correlation coefficients (r > .60) underscoring their importance. Notably, REL was found to moderate the relationships among RES, COM, and PTG, highlighting its critical role in the psychological adaptation of left-behind children. CONCLUSION: The study underscores the importance of nurturing intellectual and REs, AUT, and COM to enhance psychological REL and well-being among LBAs. These elements are crucial for supporting the mental health and developmental needs of children facing the challenges of parental migration. The findings advocate for targeted interventions that can address the unique needs of this vulnerable population, emphasizing the potential for growth and adaptation despite adversities.